PURPOSE: Despite intensive treatment regimens, overall survival for high-risk neuroblastoma (HRNB) is still poor. This is in part due to an inability to cure the disease once a patient has reached clinical relapse. Identifying plasma biomarkers of active disease may provide a way of relapse monitoring in HRNB. EXPERIMENTAL DESIGN: In this study, we developed an integrated proteomic approach to identify plasma biomarkers for HRNB. RESULTS: We identified seven candidate biomarkers (SAA, APOA1, IL-6, EGF, MDC, sCD40L and Eotaxin) for HRNB. These biomarkers were then used to create a multivariate classifier of HRNB, which showed a specificity of 90% (95% confidence interval (CI), 73%, 98%), and a sensitivity of 81% (95%CI, 54%, 96%) for classifying HRNB in a training set. When evaluated on independent test samples, the classifier exhibited 86% accuracy (95% CI, 42%, 100%) of identifying diagnostic samples, and 86% accuracy (95% CI, 70%, 100%) of detecting post-diagnosis longitudinal samples that having active disease. CONCLUSION AND CLINICAL RELEVANCE: Further validation of these biomarkers may improve patients' outcomes by developing a simple blood test for the detection of relapse prior to the development of clinically evident disease. Understanding the role of these biomarkers in immune surveillance of neuroblastoma may also provide a new direction of therapeutic strategies.
PURPOSE: Despite intensive treatment regimens, overall survival for high-risk neuroblastoma (HRNB) is still poor. This is in part due to an inability to cure the disease once a patient has reached clinical relapse. Identifying plasma biomarkers of active disease may provide a way of relapse monitoring in HRNB. EXPERIMENTAL DESIGN: In this study, we developed an integrated proteomic approach to identify plasma biomarkers for HRNB. RESULTS: We identified seven candidate biomarkers (SAA, APOA1, IL-6, EGF, MDC, sCD40L and Eotaxin) for HRNB. These biomarkers were then used to create a multivariate classifier of HRNB, which showed a specificity of 90% (95% confidence interval (CI), 73%, 98%), and a sensitivity of 81% (95%CI, 54%, 96%) for classifying HRNB in a training set. When evaluated on independent test samples, the classifier exhibited 86% accuracy (95% CI, 42%, 100%) of identifying diagnostic samples, and 86% accuracy (95% CI, 70%, 100%) of detecting post-diagnosis longitudinal samples that having active disease. CONCLUSION AND CLINICAL RELEVANCE: Further validation of these biomarkers may improve patients' outcomes by developing a simple blood test for the detection of relapse prior to the development of clinically evident disease. Understanding the role of these biomarkers in immune surveillance of neuroblastoma may also provide a new direction of therapeutic strategies.
Authors: Zhen Zhang; Robert C Bast; Yinhua Yu; Jinong Li; Lori J Sokoll; Alex J Rai; Jason M Rosenzweig; Bonnie Cameron; Young Y Wang; Xiao-Ying Meng; Andrew Berchuck; Carolien Van Haaften-Day; Neville F Hacker; Henk W A de Bruijn; Ate G J van der Zee; Ian J Jacobs; Eric T Fung; Daniel W Chan Journal: Cancer Res Date: 2004-08-15 Impact factor: 12.701
Authors: M Fevzi Ozkaynak; Andrew L Gilman; Wendy B London; Arlene Naranjo; Mitchell B Diccianni; Sheena C Tenney; Malcolm Smith; Karen S Messer; Robert Seeger; C Patrick Reynolds; L Mary Smith; Barry L Shulkin; Marguerite Parisi; John M Maris; Julie R Park; Paul M Sondel; Alice L Yu Journal: Front Immunol Date: 2018-06-18 Impact factor: 7.561